In modern society, automobiles are becoming increasingly widespread with the rapid economic growth, which imposes heavy pressures on urban traffic and causes severe traffic jams. It is an urgent issue to mitigate traffic congestions, so as to reduce travel time for automobile drivers, reduce fuel consumption, improve economic efficiency of a city and facilitate environment protection. Thus, the traffic information service system plays an important role in urban intelligent transport system. Prediction of traffic information is a core functionality of the traffic information service system, which is intended to mine history patterns of traffic information, predict urban traffic condition in near future and compensate delays in a traffic information service system. It also enables the drivers to be aware of the future traffic condition and drive in a stable mood. Furthermore, it is of significance that the prediction is based on the real-time traffic information gathering system while extending the real-time traffic information service to both the past and the future.
Currently, the rapid development of mobile communication technology and the popularization of GPS technology provide potentials for accurately gathering real-time traffic. In general, such a technology can be classified into a fixed probing technology and a mobile probing technology. The fixed probing technology involves gathering real-time traffic information and monitoring traffic conditions by fixed equipments, such as loops, RTMS (Remote Traffic Microwave Sensor) and monitoring cameras. On the other hand, the mobile probing technology comprises probe vehicle technology and probe mobile terminal technology. A probe vehicle refers to a vehicle equipped with both a GPS module and a mobile communication module; and the probe vehicle technology involves obtaining in real-time vehicle-related data such as geographical location data of a probe vehicle, uploading the data to a data center regularly via the mobile communication network, performing a map matching, path finding and traffic information fusion at a server side and, finally, disseminate real-time traffic information to the user terminals. In contrast, the mobile terminal probing technology involves obtaining cell locations of a large amount of mobile terminal users by means of base station positioning in a mobile communication network, analyzing users' behavior patterns, finding out a sequence of position points which can reflect the traffic condition, calculating real-time traffic information with reference to digital map data and providing real-time traffic information service.
However, the existing technologies for acquiring real-time traffic information cannot satisfy all user requirements. In most cases, a driver desires to know not only the current traffic condition, but also the traffic condition in the near future, so as to avoid congested roads. In addition, the current technologies for acquiring real-time traffic information suffer from a certain period of delay due to time consumptions during data transmission and system calculation, while the real-time traffic condition may vary rapidly. Therefore, the prediction of traffic information becomes particularly important in practical applications and thus becomes in recent years a topic of interest in world-wide research for intelligent transport systems.
Generally, the traffic information prediction technology establishes a suitable prediction model, such as a time sequence model, a neural network model, a Bayesian model, a fuzzy mathematical model, based on accumulated historical traffic information, so as to perform information prediction. A practical, applicable traffic information prediction system should satisfy two aspects of functionalities. First, from the perspective of time length of prediction, it is necessary to support short-term, mid-term and long-term predictions. Second, from the perspective of spatial scope of prediction, it is necessary to support traffic information prediction for the entire road network, rather than merely for arterial roads or highways. Meanwhile, the road network is complicated and has a large amount of data; and a prediction model itself is highly complicated. Thus, it is a vital but difficult research topic to achieve traffic information prediction with high performance and high accuracy.
There have been some patents and papers involving methods and models for traffic information prediction. Most of these methods, however, are based on arterial roads or highways, not complete road network, a low applicability and a relatively low model complexity. Further, most of these methods did not consider the spatial relations in a road network, but rather perform prediction modeling on each of individual roads by time series analysis, fuzzy mathematics, etc. For a few space-time prediction researches, there are also a variety of drawbacks. The related patents and papers will be introduced in the following.
Patent Document 1, “Travel-time Prediction Apparatus, Travel-time Prediction Method, Traffic Information Providing System and Program”, US Patent No. 20080097686(A1), discloses a method for traffic information prediction based on an Auto-Regression (AR) time series model. It utilizes a single link as a processing element, establishes a time series sample data for link travel time based on historical traffic condition data and set up an AR model for traffic information prediction.
Patent Document 2, “System and Method of Predicting Traffic Speed Based on Speed of Neighboring Link”, US Patent No. 20080033630(A1), discloses a method for predicting current link condition based on conditions of neighboring links. According to this solution, adjacent links at two endpoints of a single current link are calculated in advance, and then a relation between the current traveling speed on the current link and the traveling speeds on the adjacent links is derived from previous traveling speed on each of the links. Finally, the traffic condition prediction can be performed based on such a model.
Non-patent Document 1, “Traffic Flow Forecasting Using a Spatio-temporal Bayesian Network Predictor”, Proceeding of ICANN 2005, discloses a method for traffic information prediction based on a space-time Bayesian network.
Non-patent Document 2, “Space Time Modeling of Traffic Flow”, IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2002, discloses a method for space-time modeling of traffic flow. According to this method, spatial features are incorporated into a prediction model by using a matrix of weights based on distance estimation, and then a space-time auto-regression moving average model can be established for short-term prediction of traffic information.
Among the above prior art solutions, Patent Document 1 establishes, on a link basis, an auto-regression model for travel time on each link for prediction. This solution, however, only considers the time domain while completely ignoring the interrelation among the road links. Moreover, it only reflects the historical traffic characteristics of a single link while failing to represent the influence of changes in traffic conditions of neighboring links on the current link. Patent Document 2 calculates the traveling speed on the current link based on the traveling speed on adjacent links. In fact, this solution involves no prediction of future traffic condition, but only calculation of traveling speeds on neighboring links based on a known link traveling speed. However, for traffic condition, the same traveling speed may imply different levels of congestion. It is thus improper to utilize speed as a sample value. Non-patent Document 1 uses a Bayesian network which is complicated in structure and very inefficient when applied to traffic information prediction for a large scale road network. Non-patent Document 2 teaches to distinguish the levels of influences of spatial relation based on distance while ignoring the influence of a key connection node on the road traffic flow. Meanwhile, this solution measures the road condition with traffic flow only, without taking into account that different levels of roads have themselves different capacities for accommodating traffic flows.
To summarize, the existing solutions are inadequate for traffic prediction, particularly for mining spatial relations, including determining the scope of spatial influence, allocating weights for spatial influence objects, unifying criteria for evaluation of traffic condition, as well as mining the relation between the historical traffic conditions of the current road and the roads within the scope of spatial influence. Further, some of the solutions select prediction models which are not extendable, so that the system efficiency decreases exponentially with the increase in prediction scope.
Obviously, it is insufficient to only establish a traffic information prediction model based on historical data and perform time sequence analysis on a single segment. The influences of precede/succeed roads should be considered as there are strong mutual influences among the road segments in the road network. For example, a road will be very likely to be congested if its succeed road is congested, and will be very likely to be unblocked if its precede road is not congested. Thus, it is desired to establish a traffic information prediction model taking into account analysis models in both space and time domains.
The time sequence model is a common prediction and control model, which finds out statistical regularities for prediction based on historical data. A Space-Time Auto Regression Moving Average (STARMA) model is a general time sequence model considering spatial relation, which is suitable for analysis space-time statistical data. This model is applicable in various fields such as regional economics and weather forecasting analysis. A core issue in utilization of this model is how to define the spatial relation, including which object to be used in spatial analysis, how to determine a spatial scope which has influence on a spatial object, and how to determine influence weights for individual spatial objects in the scope.
The present invention is directed to a method for traffic prediction based on space-time relation with high performance and high accuracy, which takes fully into account spatial characteristics of a road traffic network